Low latency ingestion into a data system

ABSTRACT

Described herein are techniques for improving transfer of metadata from a metadata database to a database stored in a data system, such as a data warehouse. The metadata may be written into the metadata database with a version stamp, which is monotonic increasing register value, and a partition identifier, which can be generated using attribute values of the metadata. A plurality of readers can scan the metadata database based on version stamp and partition identifier values to export the metadata to a cloud storage location. From the cloud storage location, the exported data can be auto ingested into the database, which includes a journal and snapshot table.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. patent application Ser. No.17/809,931, filed Jun. 30, 2022, which is a Continuation of U.S. patentapplication Ser. No. 17/648,228, filed Jan. 18, 2022, now issued as U.S.Pat. No. 11,487,788; the contents of which are hereby incorporated byreference in their entireties.

TECHNICAL FIELD

The present disclosure generally relates to data systems, such as datawarehouses, and, more specifically, to automatic ingestion of data withlow latency.

BACKGROUND

Data systems, such as data warehouses, may be provided through a cloudplatform, which allows organizations and users to store, manage, andretrieve data from the cloud. In addition to customer business data insource tables, customers may wish to store metadata along with thesource tables. This metadata can be first stored in a metadata database,but customers typically like the metadata to be stored in the datawarehouse so that it can be easily accessed.

Some techniques for transferring the metadata data from a metadatadatabase to the data warehouse can suffer from drawbacks. First,exporting the data to a cloud storage location is typically doneperiodically (say, every 15 minutes) and thus adds a lag for when thedata can be available.

Second, some systems employ a “copy” command to transfer the data fromthe cloud storage to the data warehouse, which also necessitates the useof a running warehouse for transferring the data to the target table.This conventional approach suffers from significant drawbacks, however.For example, the “copy” command is manually initiated by a user. Thismanual initiation can cause latency issues with respect to how fresh thedata is in the target table, depending on how often the “copy” commandis initiated. This manual initiation can also cause some or all the datato be lost if the “copy” task fails. Moreover, operating a runningwarehouse for the “copy” command typically incurs large expenses.

Third, once the data is transferred to the data warehouse, it typicallyis combined with the stored data using a “merge” command after each“copy” command. However, the “merge” command can be time consuming andcomputationally expensive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which a clouddatabase system can implement streams on shared database objects,according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 shows a computing environment used for transferring metadata,according to some example embodiments.

FIG. 5 shows a flow diagram for a method for exporting data, accordingto some example embodiments.

FIG. 6 a flow diagram for a method for dynamically changing the numberof partitions, according to some example embodiments.

FIG. 7 illustrates a simplified block diagram of a system for automateddata ingestion, according to some example embodiments.

FIG. 8 is a schematic block diagram of a process of ingesting data intoa database, according to some example embodiments.

FIG. 9 shows a flow diagram of a method for data ingestion into adatabase using a journal and snapshot table, according to some exampleembodiments.

FIG. 10 illustrates an example of a database environment with a journaland snapshot table, according to some example embodiments.

FIG. 11 shows a flow diagram of a method for processing a query,according to some example embodiments.

FIG. 12 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Described herein are techniques for improving transfer of metadata froma metadata database to a database stored in a data system, such as adata warehouse. The metadata may be written into the metadata databasewith a version stamp, which is monotonic increasing register value, anda partition identifier, which can be generated using attribute values ofthe metadata. Hence, a plurality of readers can scan the metadatadatabase based on version stamp and partition id values to export themetadata to a cloud storage location. From the cloud storage location,the exported data can be auto ingested into the database, which mayinclude a journal and snapshot table. The exported data can be ingestedin the journal table where it can be immediately made available forquery processing. Then, using a background service operating at longerintervals, the exported data can be merged with the snapshot table.These different techniques can improve latency and reliability intransferring the metadata.

FIG. 1 illustrates an example shared data processing platform 100. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted from thefigures. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the shareddata processing platform 100 to facilitate additional functionality thatis not specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based database system 102, a cloud computing storage platform104 (e.g., a storage platform, an AWS® service, Microsoft Azure®, orGoogle Cloud Services®), and a remote computing device 106. Thenetwork-based database system 102 is a cloud database system used forstoring and accessing data (e.g., internally storing data, accessingexternal remotely located data) in an integrated manner, and reportingand analysis of the integrated data from the one or more disparatesources (e.g., the cloud computing storage platform 104). The cloudcomputing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based database system 102.While in the embodiment illustrated in FIG. 1 , a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based database system 102. Theremote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based database system 102 comprises an access managementsystem 110, a compute service manager 112, an execution platform 114,and a database 116. The access management system 110 enablesadministrative users to manage access to resources and services providedby the network-based database system 102. Administrative users cancreate and manage users, roles, and groups, and use permissions to allowor deny access to resources and services. The access management system110 can store shared data that securely manages shared access to thestorage resources of the cloud computing storage platform 104 amongstdifferent users of the network-based database system 102, as discussedin further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based database system 102. The compute service manager 112also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based databasesystem 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-N that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-N may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based database system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based database system102 and the cloud computing storage platform 104. The web proxy 120handles tasks involved in accepting and processing concurrent API calls,including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based database system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based database system 102 to scalequickly in response to changing demands on the systems and componentswithin network-based database system 102. The decoupling of thecomputing resources from the data storage devices 124-1 to 124-Nsupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.Additionally, the decoupling of resources enables different accounts tohandle creating additional compute resources to process data shared byother users without affecting the other users' systems. For instance, adata provider may have three compute resources and share data with adata consumer, and the data consumer may generate new compute resourcesto execute queries against the shared data, where the new computeresources are managed by the data consumer and do not affect or interactwith the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based database system102 is dynamic and supports regular changes to meet the current dataprocessing needs.

During typical operation, the network-based database system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1 , the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-N in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , a request processing service 202manages received data storage requests and data retrieval requests(e.g., jobs to be performed on database data). For example, the requestprocessing service 202 may determine the data necessary to process areceived query (e.g., a data storage request or data retrieval request).The data may be stored in a cache within the execution platform 114 orin a data storage device in cloud computing storage platform 104. Amanagement console service 204 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 204 may receive a request to execute a joband monitor the workload on the system. The stream share engine 225manages change tracking on database objects, such as a data share (e.g.,shared table) or shared view, according to some example embodiments.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based database system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based database system 102. For example, data storage device 220may represent caches in execution platform 114, storage devices in cloudcomputing storage platform 104, or any other storage device.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , execution platform 114 includesmultiple virtual warehouses, which are elastic clusters of computeinstances, such as virtual machines. In the example illustrated, thevirtual warehouses include virtual warehouse 1, virtual warehouse 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 124-1 to124-N and, instead, can access data from any of the data storage devices124-1 to 124-N within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-N. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

As mentioned, a table may have associated metadata (e.g., metadatamanager 216). Metadata for a table may be uploaded to a database storingthe table separately from the table data. FIG. 4 shows an example of acomputing environment used for transferring metadata, according to someexample embodiments. The computing environment may include a client 402,a metadata database 404, a storage 406, and a database 408.

The client 402 may be implemented as a remote computing device asdescribed above (e.g., remote software component 108). The client 402may write metadata for one or more tables in the metadata database 404.The metadata may be stored in the metadata database 404.

The metadata may be exported from the metadata database 404 to thestorage 406. The storage 406 may be provided as cloud storage. In someembodiments, the storage 406 may be a part of the data system. Forexample, the cloud storage may be provided as a S3 bucket in as part ofAmazon Web Services™.

The metadata may then be transferred from the storage 406 to thedatabase 408. The database 408 may also store the table to which themetadata pertains. Moreover, the metadata is then combined with theother metadata for the corresponding table in the database 408.

Each of these steps relating to the storing and moving of the metadatacan be complex and lead to latency and reliability issues. For example,in some conventional systems, data from the metadata database 404 isscanned periodically (e.g., every 15 minutes, 1 hour, etc.) to bewritten to the storage 406. This periodic scanning can increase latencyand can lead to reliability issues. For example, the periodic scanningis typically performed based on a wall clock time for when the data isoriginally written to the metadata database 404. That is, the datawritten in each epoch time (e.g., last 15 minutes, last 1 hour, etc.) isscanned to be exported. However, clock skew can cause data written atthe boundaries to be lost. Moreover, this periodic scanning is typicallydone by a single reader and large sets to scanned can lead to latencyissues.

Next, techniques for improved data exporting from the metadata database404 to storage 406 are described below. FIG. 5 shows a flow diagram fora method 500 for exporting data, according to some example embodiments.In an example, portions of the method 500 can be performed by the client402, metadata database 404 and storage 406 as described above withreference to FIG. 4 . At operation 502, the client may send metadata tothe metadata database. For example, a customer may create a new named“table 5” for account 1, and this information may be sent to themetadata database.

At operation 504, a VersionStamp may be generated and assigned for thenew data. For example, the VersionStamp may be generated by the metadatadatabase 404. The VersionStamp may be based on a monotonic increasingregister. That is, each piece of new data (e.g., slice of datapersistence object (DPO)) when written to the metadata database causedthe VersionStamp register to increase in value and that VersionStampvalue is assigned to the new data. For example, if data A was written tothe metadata database and it was assigned VersionStamp value 7. The nextdata written to the metadata database (say, data B) would be assignedVersionStamp value 8, and the next data written to the metadata databaseafter that (say, data C) would be assigned VersionStamp value 9, and soon.

The VersionStamp values may not be specific or related to any attributesof the data (e.g., account, table, etc.). Hence, the VersionStamp may beindependent of the type of data and can be used as a reference point forexporting data without the reliability issues associated with using atimestamps based on wall clock subject to clock skew, as describedabove.

At operation 506, a partitionID may be generated and assigned for thenew data. For example, the partitionID may be generated by the metadatadatabase 404. A maximum number of partitions may be set (e.g., 16, 32,etc.) and each piece of new data (e.g., slice of DPO) may be assigned apartition from the maximum number of partitions. The partitionID mayseparate incoming data into sets (or partitions) of data.

The partitionID may be implemented as a function of attributes of thenew data. In some embodiments, the partitionID may be a function of thedata identification (e.g., tableID) and the account identification(e.g., accountID). For example, the partitionID may be generated basedon hashing the relevant attributes modular divided by the number ofmaximum partitions (i.e., modular). In one example, the partitionID maybe generated using data identification and account identification asfollows:

partitionID=hash (tableID, accountID) % (maximum number of partitions)

At 508, the received data with the assigned VersionStamp and partitionIDmay be written to the metadata database. For example, a writer componentmay write this information to the metadata database where it is stored.A plurality of writers (e.g., writer computing resources) may beprovided, and writers may be assigned based on the partitionID. Thewriting of the data may be done at the same time as the assignment ofthe VersionStamp and/or partitionID. For example, the VersionStamp maybe based on the time the data was written to the metadata database.

At 510, a plurality of readers (e.g., reader computing resources) mayscan stored metadata in the metadata database to write the data intostorage, such as cloud storage, based on the partitionID andVersionStamp. In some embodiments, the readers may perform the scanperiodically (say, every 5 seconds) at a faster rate than conventionalsystems and scan the most current data stored in the metadata databasebased on the VersionStamps. For example, in a first scan, the readersmay scan new data up to a specific VersionStamp, say x. In the nextscan, the readers scan new data with VersionStamps starting from x+1 toy, and the next scan after that may scan new data with VersionStampsstarting from y+1 and so on. The use of VersionStamps, which aremonotonically increased, reduces the risk of skipping data as comparedto techniques that use timestamp due to clock drift issues mentionedabove and allows scanning to be done at more frequent intervals,reducing latency issues.

Moreover, the use of partitionIDs allow multiple readers to scan storeddata in parallel. A reader may be assigned one or more non-overlappingpartitions to scan. For example, a reader may be assigned one partition,such that the number of readers equals the number of partitions. In someembodiments, a reader may be assigned a plurality of partitions, suchthat the number of readers is less than the number of partitions.

A mapping of partitions to readers may be assigned. For example, thepartition-to-reader mapping can be implemented as follows:

reader-id=mod(partitionID, number of readers)

Flexibility and dynamic allocation of readers and partitions may also beimplemented. The number of partitions and/or number of readers can bechanged dynamically. However, some safeguard may be put in place toensure data accuracy and reliability.

FIG. 6 shows a flow diagram for a method 600 for dynamically changingthe number of partitions, according to some example embodiments. Atoperation 602, a first number of maximum partitions may be set, e.g., 16partitions. The first number may be based on current usage conditions.

At operation 604, a change in usage conditions may be detected. Thesystem may detect that the amount of data being sent to the metadatadatabase is changing; hence, the number of partitions should changeaccordingly. In some embodiments, thresholds may be used to detect thechange in usage conditions. For example, if usage conditions exceed ahigh threshold, this may indicate that the number of partitions shouldbe increased to facilitate the use of more parallel readers. If usageconditions fall below a low threshold, this may indicate that the numberof partitions should be decreased so as to conserve computing resources.

At 606, the system may change the number of maximum partitions to asecond value, e.g., 32 partitions based on the detected change in usageconditions. The change may be set to be implemented for a specifiedtime, e.g., time T0. At operation 608, the system may assign the numberof readers based on the new second number of partitions to go intoeffect at the specified time, e.g., T0.

However, the system may delay writing to the new partitions so that newdata is not missed. At operation 610, the system may delay writerswriting new data to the new partition assignments by some time, e.g.,T0+n, where n is a specified delay time. Thus, readers may start readingfrom the new partition assignment at substantially the same time as whenthe new second number of partitions are set, but writers may continuewriting to the first number of partitions until a specified delay. Itmay be that for the time between the change and the delay (T0−T0+n),some readers may not be in use because no data is being written to theirassigned partitions. This delay, however, ensures that data is notmissed by the writers who are writing the newly arrived data in themetadata database and generating the partitionIDs.

Safeguards may also be put into place when changing the number ofreaders. A change in the number of readers or assignment of readers maybe set based on checkpoints relating to VersionStamps. That is, thechange in reader assignment may be set to a specified VersionStamp, nota specified time. For example, consider the system, based on detectedusage conditions, increases the number of readers that scan data in themetadata DB based on partitionIDs and VersionStamps to export tostorage. The system may specify a checkpoint of a VersionStamp numberthe current assignment of readers should read until before switching thenumber of readers.

An example: the system is scanning data using 16 readers; however, thesystem changes the number of readers to 32 readers for data withVersionStamps higher than 20. Thus, the 16 currently assigned readerswill continue scanning the data until one of the 16 readers scans datawith VersionStamp 20, thus crossing the set checkpoint. After that, thecurrently assigned readers will stop scanning, and the system willchange the reader assignment to 32 readers and the 32 readers will thencontinue scanning data with VersionStamp 20 and so on based on the newreader assignments.

Referring back to FIG. 4 , the next step in the transfer of the metadatais from the storage 406 to the database 408. Some conventionaltechniques use a “copy” command for this transfer. The “copy” command istypically manually performed or performed based on a set schedule (say,every 15 minutes). However, the use of such “copy” commands can add morelatency.

Thus, this data transfer from the storage 406 to the database 408 may beimproved by implementing auto-ingestion techniques, as described infurther detail below. FIG. 7 is a simplified block diagram of system 700for automated data ingestion, according to some example embodiments. Thesystem may include a storage 702, which may be provided as cloud storage(e.g., storage 406). The storage 702 may include data that has beenexported from a metadata database using the techniques described above.

The storage 702 may store data (or files) from the metadata database tobe ingested into a database 710. In some embodiments, the storage 702may include a storage unit 702.1, an event block 702.2, and a queue702.3. The system may also include a deployment to ingest data in thedatabase 710. The deployment may be communicatively coupled to the queue702.3, and may include an integration 704, a pipe 706, and a receiver708.

Integration 704 may be configured to receive a notification when newdata becomes available in queue 702.3. For example, the queue mayinclude a pool of Simple Queue Service™ (SQS) queues as part of anAmazon Web Services™ S3 bucket. The pool of SQS queues may be providedto client accounts to add user files to a bucket. A notification may beautomatically generated when one or more user files are added to aclient account data bucket. A plurality of customer data buckets may beprovided for each client account. The automatically generatednotification may be received by the integration 704.

For example, the integration 704 may provide information relating to anoccurrence of an event in the queue 702.3. Events may include creationof new data, update of old data, and deletion of old data. Theintegration 704 may also provide identification information for aresource associated with the event, e.g., the user file that has beencreated, updated, or deleted. The integration 704 may communicate withthe queue 702.3 because the integration 704 may be provided withcredentials for the queue 702.3, for example by an administrator and/oruser. In an embodiment, the integration 704 may poll the queue 702.3 fornotifications.

The integration 704 may deliver the notification to the pipe 706, whichmay be provided as a single pipe or multiple pipes. The pipe 706 maystore information relating to what data and the location of the data forautomatic data ingestion related to the queue 702.3.

The receiver 708 may perform the automated data ingestion, and thenstore the ingested data in the database 710. Data ingestion may beperformed using the techniques described in U.S. patent application Ser.No. 16/201,854, entitled “Batch Data Ingestion in Database Systems,”filed on Nov. 27, 2018, which is incorporated herein by reference in itsentirety, including but not limited to those portions that specificallyappear hereinafter, the incorporation by reference being made with thefollowing exception: In the event that any portion of theabove-referenced application is inconsistent with this application, thisapplication supersedes the above-referenced application.

FIG. 8 is a schematic block diagram of a process 800 of ingesting datainto a database, according to some example embodiments. The process 800begins and a storage 802 sends an ingest request, such as anotification. The storage 802 may directly or indirectly communicatewith the database system to send in the ingest request. In someembodiments, the ingest request is a notification provided by athird-party vendor storage account, or the ingest request may arise froma compute service manager polling a data lake associated with the clientaccount to determine whether any user files have been added to theclient account that have not yet been ingested into the database. Thenotification includes a list of files to insert into a table of thedatabase. The files are persisted in a queue specific to the receivingtable of the database.

The ingest request is received by a compute service manager 804. Thecompute service manager 804 identifies at step 806 a user file toingest. At step 808, the compute service manager identifies a cloudprovider type associated with the client account. At step 810, thecompute service manager 804 may assign the user file to one or moreexecution nodes, based at least in part on the detected cloud providertype, and registers at step 812 micro-partition metadata associated witha database table after the file is ingested into a micro-partition ofthe database table. The compute service manager 804 provisions one ormore execution nodes 816, 820 of an execution platform 814 to performone or more tasks associated with ingesting the user file. Such ingesttasks 818 a, 818 b, 822 a, 822 b include, for example, cutting a fileinto one or more sections, generating a new micro-partition based on theuser file, and/or inserting the new micro-partition in a table of thedatabase.

The process 800 begins an IngestTask that will run on a warehouse. TheIngestTask will pull user files from the queue for a database tableuntil it is told to stop doing so. The IngestTask will periodically cuta new user file and add it to the database table. In one embodiment, theingest process is “serverless” in that it is an integrated serviceprovided by the database or compute service manager 804. That is, a userassociated with the client account need not provision its own warehouseor a third-party warehouse in order to perform the ingestion process.For example, the database or database provided (e.g., via instances ofthe compute service manager 804) may maintain the ingest warehouse thatthen services one or more or all accounts/customers of the databaseprovider.

In some embodiments, there may be more than one IngestTask pulling froma queue for a given table, and this might be necessary to keep up withthe rate of incoming data. In some embodiments, the IngestTask maydecide the time to cut a new file to increase the chances of getting anideal sized file and avoid “odd sized” files that would result if thefile size was line up with one or more user files. This may come at thecost of added complexity as the track line number of the files consumedmust be tracked.

Referring back to FIG. 4 , once the data has been transferred to thedatabase 408, it still needs to be combined with the relevant table(s)stored in the database 408. Some conventional techniques use a “merge”command for each “copy” command. That is, the database 408 would “merge”newly received data (“raw data”) with data stored in the relevant tables(“clean data”) in response to each “copy” command. However, the use ofsuch “merge” commands can be very time consuming.

Next, techniques to improve combining the newly received data (e.g.,“raw data”) with the stored data (e.g., “clean data”) so that the newlyreceived data is made available for applications, such as queryprocessing, faster are described below. The techniques may employ usingjournal and snapshot tables instead one just one table that stores alldata for a given table.

FIG. 9 shows a flow diagram of a method 900 for data ingestion into adatabase using a journal and snapshot table, according to some exampleembodiments. At operation 902, the database may receive data from astorage (e.g., storage 406) to be combined with data in a snapshottable, which includes relevant stored data (“clean data”).

At operation 904, the database may store the received data (fromingestion) into a journal table that is related to the snapshot tablebut separate from the snapshot table. Instead of the new data beingdirectly ingested and then merged with the clean data, the journal tablemay store the newly ingested data.

At operation 906, the database may make the newly ingested data in thejournal table available for application processing, such as queryprocessing, before the data is merged with the “clean data” in thesnapshot table using a view, as described in further detail below. Thus,the view may allow query processing of data in the journal table whichis not yet merged with the “clean data” in the snapshot table.

At operation 908, using a background service that operates periodically(e.g., every few hours), the data in the journal table may be merged orcombined with the clean data in the snapshot table. The backgroundservice may merge the data from the journal to the snapshot table atspecified times (e.g., intervals). After the data in the journal tableis merged in the snapshot table, that data may be removed from thejournal table. Because the data in the journal table is available forapplications, such as query processing, the background service mayoperate at longer intervals, such as every few hours, as compared toissuing a merge command immediately upon receiving the data as in othersystems described above. These techniques thus conserve computingresources because merge commands are generally very time consuming.

FIG. 10 illustrates an example of a database environment 1000 with ajournal and snapshot table, according to some example embodiments. Thedatabase environment 1000 may include a storage 1002, a database 1004with a journal table 1006, a snapshot table 1008, and a view 1010, andapplications 1012. The storage 1002 may store metadata exported from ametadata database, as described above. The data from the storage 1002may be ingested into the journal table 1006, as described above. Thesnapshot table 1008 may store “clean data,” as described above. The view1010 may be generated based on the data stored in the journal table 1006and snapshot table 1008. The view 1010 provides a homogenous interfacefor the data in the journal table 1006 and snapshot table 1008 from theviewpoint of the applications 1012. For example, a user may submit aquery using applications 1012 where the query relates to data from theboth the journal table 1006 and snapshot table 1008. The query thereforeis processed using the view 1010, as described in further detail below.

Moreover, a background service may run at specified intervals to mergethe data in journal table 1006 to the snapshot table 1008. Theseintervals may be spaced apart (e.g., hours) because the view 1010 makesthe data in the journal table 1006 available immediately after ingestionbut before merging and because merging commands are time consuming andexpensive. Reducing the number of merge commands can significantlyimprove efficiency.

FIG. 11 shows a flow diagram of a method 1100 for processing a query,according to some example embodiments. At operation 1102, the datasystem may receive a query. The query may relate to data stored in asnapshot table and data stored in a journal table, which has not yetbeen merged with the snapshot table, as described herein. Hence, thedata system may process the query using both the data stored in thesnapshot table and the journal table by generating a view, as describedherein.

At operation 1104, the view may retrieve relevant rows from the snapshottable, which do not have a corresponding more recent entry in thejournal table. At operation 1106, the view may retrieve relevant rowsfrom the journal table. For each rowID in the journal table, the viewmay retrieve the most recent entry. For example, if the journal tableincludes two entries for rowID=5, then view retrieves the most recent ofthe two entries for rowID=5.

At operation 1108, the retrieved data from the snapshot table and theretrieved data from the journal table may be joined (e.g., unionoperator) to generate the view for query execution. This joining of thesnapshot and journal table data may present a unified view of the data.At operation 1110, the query may be executed using the joined data(i.e., the view), and a result of the query may be generated andtransmitted to the requester of the query.

FIG. 12 illustrates a diagrammatic representation of a machine 1200 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1200 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 12 shows a diagrammatic representation of the machine1200 in the example form of a computer system, within which instructions1216 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1200 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1216 may cause the machine 1200 to execute anyone or more operations of any one or more of the methods describedherein. As another example, the instructions 1216 may cause the machine1200 to implement portions of the data flows described herein. In thisway, the instructions 1216 transform a general, non-programmed machineinto a particular machine 1200 (e.g., the remote computing device 106,the access management system 118, the compute service manager 112, theexecution platform 114, the access management system 110, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 1200 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1200 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1200 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1216, sequentially orotherwise, that specify actions to be taken by the machine 1200.Further, while only a single machine 1200 is illustrated, the term“machine” shall also be taken to include a collection of machines 1200that individually or jointly execute the instructions 1216 to performany one or more of the methodologies discussed herein.

The machine 1200 includes processors 1210, memory 1230, and input/output(I/O) components 1250 configured to communicate with each other such asvia a bus 1202. In an example embodiment, the processors 1210 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1212 and aprocessor 1214 that may execute the instructions 1216. The term“processor” is intended to include multi-core processors 1210 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1216 contemporaneously. AlthoughFIG. 12 shows multiple processors 1210, the machine 1200 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1230 may include a main memory 1232, a static memory 1234,and a storage unit 1236, all accessible to the processors 1210 such asvia the bus 1202. The main memory 1232, the static memory 1234, and thestorage unit 1236 store the instructions 1216 embodying any one or moreof the methodologies or functions described herein. The instructions1216 may also reside, completely or partially, within the main memory1232, within the static memory 1234, within the storage unit 1236,within at least one of the processors 1210 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1200.

The I/O components 1250 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1250 thatare included in a particular machine 1200 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1250 mayinclude many other components that are not shown in FIG. 12 . The I/Ocomponents 1250 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1250 mayinclude output components 1252 and input components 1254. The outputcomponents 1252 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1254 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1250 may include communication components 1264operable to couple the machine 1200 to a network 1280 or devices 1270via a coupling 1282 and a coupling 1272, respectively. For example, thecommunication components 1264 may include a network interface componentor another suitable device to interface with the network 1280. Infurther examples, the communication components 1264 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1270 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1200 may correspond to any one ofthe remote computing device 106, the access management system 118, thecompute service manager 112, the execution platform 114, the accessmanagement system 110, the Web proxy 120, and the devices 1270 mayinclude any other of these systems and devices.

The various memories (e.g., 1230, 1232, 1234, and/or memory of theprocessor(s) 1210 and/or the storage unit 1236) may store one or moresets of instructions 1216 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1216, when executed by theprocessor(s) 1210, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1280may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1280 or a portion of the network1280 may include a wireless or cellular network, and the coupling 1282may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1282 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1216 may be transmitted or received over the network1280 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1264) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1216 may be transmitted or received using a transmission medium via thecoupling 1272 (e.g., a peer-to-peer coupling) to the devices 1270. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1216 for execution by the machine 1200, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: receiving data into a metadata database;generating a version stamp for the received data; generating a partitionidentifier for the received data, the partition identifier being basedon one or more attribute values of the received data; using one or morewriter computing resources, writing the received data into the metadatadatabase with the generated version stamp and partition identifier; andusing a plurality of reader computing resources, scanning the metadatadatabase and exporting the received data to a storage location based onthe version stamp and partition identifier, the plurality of readersbeing assigned based on partition identifiers.

Example 2. The method of example 2, wherein the partition identifier isgenerated using data identification and account identification values.

Example 3. The method of any of examples 1-2, wherein generating thepartition identifier includes hashing the data identification andaccount identification values.

Example 4. The method of any of examples 1-3, further comprising:

-   -   changing a number of partitions, including: setting a first        number of partitions; detecting a change in usage conditions;        based on the detected change in usage conditions, setting a        second number partitions to go into effect at a specified time;        and assigning the plurality of reader computing resources based        on the second number of partitions to scan the metadata database        starting at the specified time.

Example 5. The method of any of examples 1-4, further comprising:assigning the one or more writer computing resources to write new datato the second number partitions starting at the specified time plus adelay.

Example 6. The method of any of examples 1-5, further comprising:changing a number of reader computing resources, the changing of thenumber of reader computing resources including: setting a first numberof reader computing resources; detecting a change in usage conditions;and based on the detected change in usage conditions, setting a secondnumber of reader computing resources, wherein the second number ofcomputing resources is set to go into effect based on a checkpointrelating to version stamp values.

Example 7. The method of any of examples 1-6, further comprising:receiving a notification from the storage indicating that the exporteddata is stored in the storage; and in response to the notification,ingesting the exported data from the storage into a database.

Example 8. The method of any of examples 1-7, further comprising:ingesting the exported data into a journal table in the database; makingthe exported data in the journal table accessible for query processingbefore the exported data is merged with a snapshot table; and using abackground service operating at specified intervals, merging theexported data from the journal table into the snapshot table.

Example 9. The method of any of examples 1-8, wherein the version stampis a monotonic increasing register value.

Example 10. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 9.

Example 11. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 9.

What is claimed is:
 1. A method comprising: receiving data into ametadata database; generating a version stamp for the received data;generating a partition identifier for the received data, the partitionidentifier being based on one or more attribute values of the receiveddata; writing, by one or more writer computing resources, the receiveddata into the metadata database with the version stamp and partitionidentifier; scanning, by a plurality of reader computing resources, themetadata database; exporting the received data to a storage locationbased on the version stamp and partition identifier, the plurality ofreaders being assigned based on partition identifiers; ingesting theexported data into a journal table in the database; and providing theexported data in the journal table accessible for query processingbefore the exported data is merged with a snapshot table.
 2. The methodof claim 1, wherein the partition identifier is generated using dataidentification and account identification values.
 3. The method of claim2, wherein generating the partition identifier includes hashing the dataidentification and account identification values.
 4. The method of claim1, further comprising: changing a number of partitions, including:setting a first number of partitions; detecting a change in usageconditions; based on the detected change in usage conditions, setting asecond number partitions to go into effect at a specified time; andassigning the plurality of reader computing resources based on thesecond number of partitions to scan the metadata database starting atthe specified time.
 5. The method of claim 4, further comprising:assigning the one or more writer computing resources to write new datato the second number partitions starting at the specified time plus adelay.
 6. The method of claim 1, further comprising: changing a numberof reader computing resources, the changing of the number of readercomputing resources including: setting a first number of readercomputing resources; detecting a change in usage conditions; and basedon the detected change in usage conditions, setting a second number ofreader computing resources, wherein the second number of computingresources is set to go into effect based on a checkpoint relating toversion stamp values.
 7. The method of claim 1, further comprising:receiving a notification from the storage location indicating that theexported data is stored in the storage location; and in response to thenotification, ingesting the exported data from the storage location intoa database.
 8. The method of claim 7, further comprising: merging, by abackground service, the exported data from the journal table into thesnapshot table.
 9. The method of claim 1, wherein the version stamp is amonotonic increasing register value.
 10. A machine-storage mediumembodying instructions that, when executed by a machine, cause themachine to perform operations comprising: receiving data into a metadatadatabase; generating a version stamp for the received data; generating apartition identifier for the received data, the partition identifierbeing based on one or more attribute values of the received data;writing, by one or more writer computing resources, the received datainto the metadata database with the version stamp and partitionidentifier; scanning, by a plurality of reader computing resources, themetadata database; exporting the received data to a storage locationbased on the version stamp and partition identifier, the plurality ofreaders being assigned based on partition identifiers; ingesting theexported data into a journal table in the database; and providing theexported data in the journal table accessible for query processingbefore the exported data is merged with a snapshot table.
 11. Themachine-storage medium of claim 10, wherein the partition identifier isgenerated using data identification and account identification values.12. The machine-storage medium of claim 11, wherein generating thepartition identifier includes hashing the data identification andaccount identification values.
 13. The machine-storage medium of claim10, further comprising: changing a number of partitions, including:setting a first number of partitions; detecting a change in usageconditions; based on the detected change in usage conditions, setting asecond number partitions to go into effect at a specified time; andassigning the plurality of reader computing resources based on thesecond number of partitions to scan the metadata database starting atthe specified time.
 14. The machine-storage medium of claim 13, furthercomprising: assigning the one or more writer computing resources towrite new data to the second number partitions starting at the specifiedtime plus a delay.
 15. The machine-storage medium of claim 10, furthercomprising: changing a number of reader computing resources, thechanging of the number of reader computing resources including: settinga first number of reader computing resources; detecting a change inusage conditions; and based on the detected change in usage conditions,setting a second number of reader computing resources, wherein thesecond number of computing resources is set to go into effect based on acheckpoint relating to version stamp values.
 16. The machine-storagemedium of claim 10, further comprising: receiving a notification fromthe storage location indicating that the exported data is stored in thestorage location; and in response to the notification, ingesting theexported data from the storage location into a database.
 17. Themachine-storage medium of claim 16, further comprising: merging, by abackground service, the exported data from the journal table into thesnapshot table.
 18. The machine-storage medium of claim 10, wherein theversion stamp is a monotonic increasing register value.
 19. A systemcomprising: at least one hardware processor; and at least one memorystoring instructions that, when executed by the at least one hardwareprocessor, cause the at least one hardware processor to performoperations comprising: receiving data into a metadata database;generating a version stamp for the received data; generating a partitionidentifier for the received data, the partition identifier being basedon one or more attribute values of the received data; writing, by one ormore writer computing resources, the received data into the metadatadatabase with the version stamp and partition identifier; scanning, by aplurality of reader computing resources, the metadata database;exporting the received data to a storage location based on the versionstamp and partition identifier, the plurality of readers being assignedbased on partition identifiers; ingesting the exported data into ajournal table in the database; and providing the exported data in thejournal table accessible for query processing before the exported datais merged with a snapshot table.
 20. The system of claim 19, wherein thepartition identifier is generated using data identification and accountidentification values.
 21. The system of claim 20, wherein generatingthe partition identifier includes hashing the data identification andaccount identification values.
 22. The system of claim 19, theoperations further comprising: changing a number of partitions,including: setting a first number of partitions; detecting a change inusage conditions; based on the detected change in usage conditions,setting a second number partitions to go into effect at a specifiedtime; and assigning the plurality of reader computing resources based onthe second number of partitions to scan the metadata database startingat the specified time.
 23. The system of claim 22, the operationsfurther comprising: assigning the one or more writer computing resourcesto write new data to the second number partitions starting at thespecified time plus a delay.
 24. The system of claim 19, the operationsfurther comprising: changing a number of reader computing resources, thechanging of the number of reader computing resources including: settinga first number of reader computing resources; detecting a change inusage conditions; and based on the detected change in usage conditions,setting a second number of reader computing resources, wherein thesecond number of computing resources is set to go into effect based on acheckpoint relating to version stamp values.
 25. The system of claim 19,the operations further comprising: receiving a notification from thestorage location indicating that the exported data is stored in thestorage location; and in response to the notification, ingesting theexported data from the storage location into a database.
 26. The systemof claim 25, the operations further comprising: merging, by a backgroundservice, the exported data from the journal table into the snapshottable.
 27. The system of claim 19, wherein the version stamp is amonotonic increasing register value.